Measuring predictability: theory and macroeconomic applications

Francis Diebold () and Lutz Kilian ()

Journal of Applied Econometrics, 2001, vol. 16, issue 6, pages 657-669

Abstract: We propose a measure of predictability based on the ratio of the expected loss of a short-run forecast to the expected loss of a long-run forecast. This predictability measure can be tailored to the forecast horizons of interest, and it allows for general loss functions, univariate or multivariate information sets, and covariance stationary or difference stationary processes. We propose a simple estimator, and we suggest resampling methods for inference. We then provide several macroeconomic applications. First, we illustrate the implementation of predictability measures based on fitted parametric models for several US macroeconomic time series. Second, we analyze the internal propagation mechanism of a standard dynamic macroeconomic model by comparing the predictability of model inputs and model outputs. Third, we use predictability as a metric for assessing the similarity of data simulated from the model and actual data. Finally, we outline several non-parametric extensions of our approach. Copyright © 2001 John Wiley & Sons, Ltd.

Date: 2001
View list of references View citations in EconPapers

Downloads: (external link)
http://qed.econ.queensu.ca:80/jae/2001-v16.6/ Supporting data files and programs (text/html)

Related works:
Working Paper: Measuring Predictability: Theory And Macroeconomic Applications (2000)
Working Paper: Measuring predictability: theory and macroeconomic applications (1997)
Working Paper: Measuring Predictability: Theory and Macroeconomic Applications (1997)
Working Paper: Measuring Predictability: Theory and Macroeconomic Applications (1998)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:jae:japmet:v:16:y:2001:i:6:p:657-669

Ordering information: This journal article can be ordered from
http://www3.intersci ... e.jsp?issn=0883-7252

Access Statistics for this article

Journal of Applied Econometrics is edited by M. Hashem Pesaran

More articles in Journal of Applied Econometrics from John Wiley & Sons, Ltd.
Series data maintained by Christopher F. Baum ().